Why Standard HTTP Error Handling Fails for LLM APIs

Backend engineers typically handle HTTP errors using generic retry logic with exponential backoff, but this approach breaks down when applied to large language model APIs. LLM providers reuse standard status codes like 429 and 500, yet the underlying causes vary widely — from temporary rate limits and model overload to hard quota exhaustion and billing issues — each requiring a different response. Blindly retrying timed-out LLM requests can duplicate side effects in agent workflows, inflate token costs, and degrade user experience rather than improve reliability. Operations such as tool-calling agents, streaming chats, and structured output generation each carry different retry risks that a one-size-fits-all handler cannot address. Developers are advised to build retry logic that accounts for the specific LLM operation type and the precise error category, not just the HTTP status code.